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Open AccessProceedings ArticleDOI

The new Casper: query processing for location services without compromising privacy

TLDR
Zhang et al. as mentioned in this paper presented Casper1, a new framework in which mobile and stationary users can entertain location-based services without revealing their location information, which consists of two main components, the location anonymizer and the privacy-aware query processor.
Abstract
This paper tackles a major privacy concern in current location-based services where users have to continuously report their locations to the database server in order to obtain the service. For example, a user asking about the nearest gas station has to report her exact location. With untrusted servers, reporting the location information may lead to several privacy threats. In this paper, we present Casper1; a new framework in which mobile and stationary users can entertain location-based services without revealing their location information. Casper consists of two main components, the location anonymizer and the privacy-aware query processor. The location anonymizer blurs the users' exact location information into cloaked spatial regions based on user-specified privacy requirements. The privacy-aware query processor is embedded inside the location-based database server in order to deal with the cloaked spatial areas rather than the exact location information. Experimental results show that Casper achieves high quality location-based services while providing anonymity for both data and queries.

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Citations
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Proceedings ArticleDOI

Preserving location privacy for location-based services with continuous queries on road network

TL;DR: This paper presents the privacy safety condition to defend against velocity-based attack, and proposes an anonymity algorithm based on greedy strategy to preserve user privacy that is effective and feasible.
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A path-based access control method for location obfuscation in mobile environment

TL;DR: The path-based access control model for location obfuscation can effectively protect location privacy in mobile environment, and it is more accurate than the traditional one.
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Best Practices for Web Development using Grails and Django

TL;DR: This paper is focused on identifying and providing according to the experiences and requirements of the users, the best practices for Web development by using Grails and Django Web frameworks to develop more interactive and efficient Web applications.
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Privacy in context-aware mobile crowdsourcing systems

TL;DR: This paper demonstrates specific threats of continuous sharing of users locations in push-based crowd-sourcing platforms and proposes a simple yet effective location perturbation technique that obfuscates certain user locations to achieve privacy guarantees while not affecting the quality of the recommendations the system generates.
Proceedings ArticleDOI

ReverseCloak: A Reversible Multi-level Location Privacy Protection System

TL;DR: A toolkit for ReverseCloak, a location perturbation system to protect location privacy over road networks in a multi-level reversible manner, consisting of an `Anonymizer' GUI to adjust the anonymization settings and visualize the multilevel cloaking regions over road network for location data owners and a `De-anonymizer’ GUI to de-anonymsize the cloaking region.
References
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Journal ArticleDOI

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TL;DR: The solution provided in this paper includes a formal protection model named k-anonymity and a set of accompanying policies for deployment and examines re-identification attacks that can be realized on releases that adhere to k- anonymity unless accompanying policies are respected.
Proceedings ArticleDOI

Anonymous Usage of Location-Based Services Through Spatial and Temporal Cloaking

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TL;DR: This paper addresses the problem of releasing microdata while safeguarding the anonymity of respondents to which the data refer and introduces the concept of minimal generalization that captures the property of the release process not distorting the data more than needed to achieve k-anonymity.
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TL;DR: This paper provides a formal presentation of combining generalization and suppression to achieve k-anonymity and shows that Datafly can over distort data and µ-Argus can additionally fail to provide adequate protection.
Journal ArticleDOI

Location privacy in pervasive computing

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